1. Field of the Disclosure
The disclosure relates generally to social networking methods and systems and specifically in certain embodiments to methods and systems for proximity-driven social networking applications implemented with autonomous mobile agents incorporating data mining and machine learning classification.
2. General Background
Mobile device users (agents) provide a platform for a social networking interaction that is motivated by groupings of proximate users, independent of other typical social network connections. Geographical proximity, and additional features proximity, provides categorizations which establish a set of proximate agents which are thus associated for interaction.
The information exchange between any two agents may be performed with open identity, or with partial or complete anonymity
Types of data exchanged may be user-driven communication, user-defined auto-categorizations, and automatic machine classification operating on feature data inputs from associated agents.
Data mining and machine learning technologies may be used to develop automatic computer constructs for agents whom can operate on feature data sets from associated neighbor agents as input. Computer machine constructs within an agent perform classification operations on feature data sets from associated agents and the classification results may be communicated back to associated agents. This information exchange may be autonomously derived.
As an example, facial recognition technology (FRT) provides a means to derive feature data to describe the facial appearance of a user agent. Machine learning provides a technology by which the facial attraction preferences of an associated (proximate group) agent may be modeled. The computer machine of the associated agent may operate on the feature data set from the parent agent and thus classify the associated agent as attracted, or not attracted to the parent agent. This process allows the associated agents to be thusly classified. Classification using facial recognition technology (FRT) and machine learning is one example. Other classifications may be implemented into this structure.
It is desirable to address the limitations in the art, e.g., to apply methods and systems for proximity-driven social networking applications implemented with autonomous mobile agents incorporating data mining and machine learning classification.